A goal of business is to explain sales volumes results so as to understand how marketing activities affect the sales volumes, e.g., how much volume these marketing activities contributed to overall sales volume. These volume contributions are useful to calculate effectiveness measures for the activities such as volume per dollar spend or return on investment (ROI). Examples of such activities are activities directly controlled by the business (e.g., our TV advertising, a display for our products, a price increase for our products), activities controlled by another businesses in the market (e.g., a competitor's display, competitor's TV advertising, etc.), and/or the environment itself (e.g. a cold spell, a gas-price increase, etc.). For example, a company may want to know the approximate change in future sales, growth, and profit of the product or service based on these activities or changes in these activities. In addition, many companies want to know the effects of changes in these activities (e.g., marketing, advertising, pricing changes, etc.) on forecasted data (e.g., sales volume growth, profit, etc.) that are dependent on these activities.
In addition to being applied to a sales volume, these same techniques are used to interpret the effect of activities on other measurable business metrics, e.g., revenue, profit or market share, etc.
Typically, an analyst uses a mathematical model to estimate how these activities affect sales volumes in the past, or in the future. An example is a regression-based model. A regression-based model will typically relate the sales volumes to each of the activities via a coefficient. The analyst determines the coefficients based on the regression model (or another multivariate technique). The analyst then interprets the coefficient to assign rates of volume changes for each activity. For example, an analyst would determine that for one unit of an activity, such as promotion, would equal X percent or Y units change in sales volume. The analyst then multiplies this coefficient to the change in the amount of the activity, and a corresponding amount of volume is calculated. By doing this analysis for each of the activity/coefficient pairs, the analyst predicts and explains the effect of the activity on volume (or another relevant measurable business metric, like profit, etc.). In addition, in order to explain volume changes across time periods, the analyst would calculate volume contributions by activity for each time period and report the difference as explanation of volume change.
A problem with this approach is that the interpretation of the derived coefficients is dependent on the model that is used. For example, a price coefficient in a linear model and a multiplicative model for the same volume and activities will differ significantly from each other. This makes aggregation across products or channels for which different types of models were used difficult and requires volume interpreting algorithms specific to the model form used. Furthermore, the rates of volume change are dependent on the set of activities chosen. In addition, the results are inconsistent when using different sets of activities for the same time period. Moreover, some activities do not have natural reference values upon which to base the volume contribution calculations (e.g., price, distribution), and consequently, make it difficult to determine the effect of these activities on the volume and volume change across time periods.